Robotics - Localization & Bayesian Filtering - AIRLab - Politecnico di ...
Robotics - Localization & Bayesian Filtering - AIRLab - Politecnico di ...
Robotics - Localization & Bayesian Filtering - AIRLab - Politecnico di ...
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Introduction Taxonomy Probability Recall Bayes Rule <strong>Bayesian</strong> <strong>Filtering</strong> Markov <strong>Localization</strong>6/58<strong>Localization</strong> - IssuesDirect pose sensingUsually impossibleNoise corruptionPose estimationInferred from dataUsually a single sensor measure is insufficientRobot need to integrate information over timee.g. map with two identical corridorsMapVarious representations are possibleaccor<strong>di</strong>ng to the problemKey concept: localization needs a precise map
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